ILPbased maximum likelihood genome scaffolding
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Abstract
Background
Interest in de novo genome assembly has been renewed in the past decade due to rapid advances in highthroughput sequencing (HTS) technologies which generate relatively short reads resulting in highly fragmented assemblies consisting of contigs. Additional longrange linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Due to library preparation artifacts and erroneous mapping of reads originating from repeats, scaffolding remains a challenging problem. In this paper, we provide a scalable scaffolding algorithm (SILP2) employing a maximum likelihood model capturing read mapping uncertainty and/or nonuniformity of contig coverage which is solved using integer linear programming. A NonSerial Dynamic Programming (NSDP) paradigm is applied to render our algorithm useful in the processing of larger mammalian genomes. To compare scaffolding tools, we employ novel quantitative metrics in addition to the extant metrics in the field. We have also expanded the set of experiments to include scaffolding of lowcomplexity metagenomic samples.
Results
SILP2 achieves better scalability throughg a more efficient NSDP algorithm than previous release of SILP. The results show that SILP2 compares favorably to previous methods OPERA and MIP in both scalability and accuracy for scaffolding single genomes of up to human size, and significantly outperforms them on scaffolding lowcomplexity metagenomic samples.
Conclusions
Equipped with NSDP, SILP2 is able to scaffold large mammalian genomes, resulting in the longest and most accurate scaffolds. The ILP formulation for the maximum likelihood model is shown to be flexible enough to handle metagenomic samples.
Keywords
Integer Linear Program Read Pair Integer Linear Program Formulation Metagenomic Sample Maximum Likelihood ModelList of abbreviations
 • ILP
 Integer Linear Programming
 • MCC
 Matthew's correlation coefficient
 • MIP
 Mixed Integer Programming
 • NSDP
 Nonserial Dynamic Programming
 • OGDF
 Open Graph Drawing Framework
 • SQPR
 a tree data structure used in graph algorithms to represent the 3connected components of a graph [29]
Introduction
De novo genome assembly is one of the best studied problems in bioinformatics. Interest in the problem has been renewed in the past decade due to rapid advances in highthroughput sequencing (HTS) technologies, which have orders of magnitude higher throughput and lower cost compared to classic Sanger sequencing. Indeed, topoftheline instruments from Illumina and Life Technologies are currently able to generate in a single run billions of reads with an aggregate length of hundreds of gigabases, at a cost of mere cents per megabase. However, most HTS technologies generate relatively short reads, significantly increasing the computational difficulty of the assembly problem. Despite much work on improved assembly algorithms for HTS shotgun reads [1, 2, 3, 4, 5, 6], de novo assembly remains challenging, often resulting in highly fragmented assemblies, see [7, 8, 9, 10, 11, 12, 13, 14] for recent reviews and benchmarking results. For example, the recent Assemblathon 2 community effort to benchmark de novo genome assemblers [7] shows that the performance of evaluated assemblers is highly variable from dataset to dataset and generally degrades with the complexity of the sample.
To increase the utility of such fragmented assemblies, additional longrange linkage information is typically used to orient, order, and link contigs into larger structures referred to as scaffolds. Although longrange linkage information can be generated using a variety of technologies, including Sanger sequencing of both ends of cloned DNA fragments of up to hundreds of kilobases, Pacific Biosciences reads of up to tens of kilobases [15], and optical maps [16], the most common type of data used in scaffolding are HTS read pairs generated from DNA fragments with length ranging between hundreds of bases to tens of kilobases. While HTS read pairs are relatively easy to generate, the linkage information they provide is noisy due to library preparation artifacts and erroneous mapping of reads originating from repeats. The general scaffolding problem is known to be computationally NPhard when linkage data contains errors [17]. Moreover, the associated contig orientation and contig ordering problems are intractable as well: the orientation problem is equivalent to finding a maximum bipartite subgraph, whereas the ordering problem is similar to the Optimal Linear Arrangement problem, both of which are NPhard [18]. Due to the intractability of the problem, greedy heuristics have been employed in practical scaffolding methods such as [17, 19]. Scaffolding methods such as SOPRA [20] reduce the size of the problem by iteratively removing inconsistent links and contigs, while MIP [21] heuristically partitions the biconnected components of the scaffolding graph when they are too large to scaffold optimally by mixed integer programming. In SLIQ [22], inequalities are derived from the geometry of contigs to predict the orientation and ordering of adjacent contigs. To find a feasible solution with minimum read pair inconsistency, OPERA [23] provides a novel dynamic programming algorithm.
Algorithms based on explicit statistical models are currently gaining popularity in the area of genome assembly [24], with notable advances in the use of maximum likelihood (ML) methods for both contig assembly [25] and assembly evaluation [26]. In this paper we introduce a highly scalable algorithm based on likelihood maximization for the scaffolding problem. The key step in our algorithm is the selection of contig orientations and a set of read pairs consistent with these orientations (and locally consistent with each other) such that the overall likelihood of selected pairs is maximized. As in previous works [25, 26], the likelihood model we employ assumes independence of the HTS read pairs. The currently implemented model takes into account read mapping uncertainty due to overlap with annotated contig repeats as well as variations in contig coverage. The model can be easily extended to incorporate sequencing errors and the distribution of insert lengths; currently we only use the latter to eliminate read pairs with highly discordant insert length lowerbounds and to compute ML estimates for the final gap lengths. Likelihood maximization is formulated as an integer linear program (ILP). Unlike MIP [21], our ILP formulation selects contig orientations and a set of locally consistent read pairs but neither explicitly orders the contigs nor fully guarantees global consistency of selected pairs. The latter are achieved by decomposing the set of selected read pairs into linear paths via bipartite matching.
Scalability of our algorithm, referred to as SILP2, is achieved by adopting a nonserial dynamical programming (NSDP) approach [27]. Rather than solving one large ILP, several smaller ILPs can be solved seperately and composed to find the complete and optimal solution. The order in which the smaller ILPs are solved is determined by the 3connected components of the underlying scaffolding graph, which can be efficiently identified in linear time via the SPQRtree data structure [28, 29].
Compared to the preliminary version of the algorithm published in [30], referred to as SILP1, SILP2 is based on explicit formalization of likelihood maximization as the optimization objective. We present experiments with several likelihood models capturing read mapping uncertainty and/or nonuniformity of contig coverage. SILP2 also achieves higher scalability by using a more efficient NSDP algorithm than SILP1. This greatly reduces the need for heuristics such as the hierarchical scaffolding approach of SILP1, whereby scaffolding is performed by progressively decreasing the minimum bound on the size of read pair bundles. We have also expanded the set of experiments to include scaffolding of lowcomplexity metagenomic samples. The results show that SILP2 compares favorably to previous methods OPERA and MIP in both scalability and accuracy for scaffolding single genomes of up to human size, and significantly outperforms them on scaffolding lowcomplexity metagenomic samples.
Methods
Scaffolding graph. The scaffolding problem is modeled with a scaffolding graph G = (V, E), where each node i ε V represents a contig and each edge (i, j) ε E represents all read pairs whose two individual reads are mapped to the contigs i and j, respectively. Each read in a pair is aligned either to the forward or reverse strand of corresponding contig sequence, and this results in 4 possible configurations for a read pair (denoted A, B, C, or D, see Figure S1 in Additional file 1) which can be modeled as a bidirected edge [23, 30, 33]. Orientation of contigs and the bidirected orientation of edges should agree (be concordant) with each other and should not result in any directed cycles for linear genomes (e.g. eukaryotes).
over all contig orientations O.
 1.
Overlap with repeats. As noted above, only pairs for which both reads map uniquely to the set of contigs are used for scaffolding. Still, a read that fully or partially overlaps a genomic repeat may be uniquely mapped to the incorrect location in case repeat copies are collapsed. We preprocess contigs to annotate repeats from known repeat families and by recording the location of multimapped reads. An estimate of the repeatbased mapping probability ${p}_{r}^{rep}$ is found by taking the percentage of bases of r aligned to nonrepetitive portions of the contigs.
 2.
Contig coverage dissimilarity. Although sequencing coverage can have significant departures from uniformity due to biases introduced in library preparation and sequencing, the average coverage of adjacent contigs is expected to be similarly affected by such biases (all read alignments, including randomly allocated nonunique alignments, are used for estimating computing average contig coverages). If the two reads of r map to contigs i, respectively j, the coveragebased mapping probability of r, ${p}_{r}^{cov}$, is defined as 1 − coverage_{ i } − coverage_{ j } /(coverage_{ i } + coverage_{ j }).
Note that factors such as repeat content of the sequenced genome and sequencing depth will determine how informative repeatbased and coveragebased mapping probabilities are. Depending on these factors, either ${p}_{r}^{rep}$, ${p}_{r}^{cov}$, or their product may provide the most accurate estimate for p_{r}. Mismatches and indels in read alignments, that can be caused by sequencing errors or polymorphisms in the sequenced sample, can easily be incorporated in the estimation of mapping probabilities.

a binary variable S_{i} for each contig i, with S_{i} equal to 0 if the contig's orientation remains the same and S_{i} = 1 if the contig's orientation is flipped w.r.t. default orientation in the final scaffold.

a binary variable S_{ij} for each edge (i, j) ε E, which equals 0 if none or both ith and jth contigs are flipped, and equals 1 if only one of them is flipped.

binary variables A_{ij} (respectively, B_{ij} , C_{ij} , and D_{ij}) which are set to 1 if and only if an edge in state A (respectively, B, C, or D) is used to connect contigs i and j (see Figure S1 in Additional file 1). For any contig pair i and j, at most one of these variables can be one.
with ${B}_{ij}^{w}$, ${C}_{ij}^{w}$ and ${D}_{ij}^{w}$ defined analogously.
In this ILP, constraints (35) enforce agreement between contig orientation variables Si's and edge orientation variables S_{ij}'s, A_{ij}'s, B_{ij}'s, C_{ij}'s, and D_{ij}'s.
Since eukaryotic genomes are linear, a valid scaffold orientation should not contain any cycles. The constraints (5) already forbid 2cycles. Additionally, 3cycles are forbidden with the constraints shown in Figure S2 in Additional file 1. Larger cycles generated in the ILP solution are broken heuristically because it is infeasible to forbid all of them using explicit constraints.
Nonserial dynamic programming. For large mammalian genomes, the number of variables and constraints is too large for solving the ILP (2)(5) via standard solvers (SILP2 uses CPLEX [34] which is available free of charge for academic institutions). We adopt the nonserial dynamic programming (NSDP) paradigm to overcome this barrier and to optimally solve the problem. NSDP is based on the interaction graph with nodes corresponding to ILP variables and edges corresponding to the ILP constraints  two nodes are adjacent in the interaction graph if their associated variables appear together in the same constraint. Through the NSDP process, variables are removed in the way that adjacent vertices can be merged together [27]. The first step in NSDP is identifying weakly connected components of the interaction graph. We find the 2 and 3connected components of the interaction graph with efficient algorithms and then we solve each component independently in such a way that the solutions can be merged together to find the global solution.
All constraints (35) as well 3cycle constraints connect S_{i}'s following the edges of the scaffolding graph. Therefore, the S_{i}nodes of the interaction graph for our ILP will have the same connectivity structure as the scaffolding graph G = (V, E). As it has been noticed in [23], the scaffolding graph is a boundedwidth graph and should be well decomposable in 2 and 3connected components. The SPQRtree data structure is employed to determine the decomposition order for 3connected components the scaffolding graph [28]. The solution to each component of the scaffolding graph is found using a bottom up traversal through which each component is solved 2 times: for similar and opposite orientations of the common nodes. The objective value of each case is then entered into the objective of the parental component. Having the solution of all components, top down DFS starting from the same root is performed to apply the chosen solution for each component.
 (1)
the ILP solution sol_{00} in which the 2cut nodes i and j are constrained to both have default orientations;
 (2)
the ILP solution sol_{01} in which the 2cut nodes i and j are constrained to have opposite orientations.
The two solutions are combined to solve the ILP for component B. The ILP objective for component B should be updated by adding the term of sol_{00}+(sol_{01}sol_{00}) · S_{ij} or, equivalently, the value sol_{00} should be added to ${A}_{ij}^{w}$ and ${D}_{ij}^{w}$ and the value sol_{01} should be added to ${B}_{ij}^{w}$ and ${C}_{ij}^{w}$. The overall solution is obtained by identifying the common nodes of the components. In the example on Figure 2(b), the optimal solution happens when 2cut nodes have opposite directions. The corresponding solution of ILP for the component A should be incorporated in the overall solution. When the scaffolding graph has 3connected components too large to handle, 3cuts could also be used for decomposition.
Thinning heuristic. Unfortunately the largest triconnected component may still induce an ILP too large for CPLEX to solve in a reasonable amount of time. In order to address this problem a thinning heuristic is applied to the scaffolding graph. This scenario can be detected by setting a threshold on the maximum number of contigs allowed in a triconnected component. When a component exceeds the threshold the number of read pairs necessary to induce an edge is increased by one and decomposition recomputed until there is no component above the threshold.
Results and discussion
Datasets and quality measures
In order to asses the quality and scalability of our scaffolding tool we developed a testing framework which closely mimics real world scaffolding problems. We utilized the Staphylococcus aureus (staph), Rhodobacter sphaeroides (rhodo) genomes and chromosome 14 of HapMap individual NA12878 (chr14) from the GAGE [14] assembly comparison. Finally, in a test case designed to stress scalability, contigs from a draft assembly of individual NA12878 (NA) created by [35] were scaffolded using shortread data.
In all test cases the read pairs used for scaffolding are aligned against the contigs using bowtie2 [36]. Each read in a pair was required to be aligned uniquely according to the default scoring scheme, for the pair to be considered valid. Each scaffolder was given the same set of valid read pairs. Two of the leading external scaffolding tools MIP [21] and OPERA [37] are used in this comparison. Although many other tools do exist, these two are widely utilized and actively maintained.
The three small test cases are used to test both correctness and scalability of the scaffolding tool. In order to test correctness, contigs simulating a draft assembly were created by placing gaps in the genome. The contig and gap sizes were sampled uniformly at random from the collection of all the assemblies used in the GAGE comparison. The procedure to generate the contigs was to alternatively sample with replacement from the set of all contig sizes, and gap sizes. In this way a simulated scaffold can be generated so that the position and relative orientation of all contigs and all gap sizes are known. The orientation of the simulated contigs was randomized to prevent biases.
For each genome 10 replicates were created, all subsequent results are the average of the 10 replicates. By creating simulated contigs with no assembly error, the accuracy of subsequent scaffolds can be evaluated exactly. Although the contigs were simulated, real read pairs were aligned against them and used as input. Table S1 in Additional File 1 describes the characteristics of each dataset.
The NA12878 test case was produced by simply using the contigs created in the SGA [35] assembler publication. The read pairs were obtained from a different lab, however they were generated using the same biological source material (ERP002490). Although more read pairs were available a random subset of approximately 2x coverage was used.
Finally a simulated metagenomics test case was created to explore the feasibility of utilizing SILP2 to scaffold metagenomes. This was created by artificially mixing the staph and rhodo contigs and reads at varying proportions.
A natural and common parameter present in all scaffolding algorithms in the bundle size, or the number of read pairs spanning two contigs. This parameter is a natural control of sensitivity and specificity; requiring more support increases specificity at the price of sensitivity and viceversa. It should be noted that every scaffolding tool tested, including SILP2 does not abide by the set parameter absolutely. Each method raises it in order to ensure efficient operation. The simulated test cases were evaluated at several bundle sizes to asses its effect on accuracy and scalability. The NA12878 test case was only evaluated at the minimum feasible value due to resource constraints.
Accuracy
Calculating the accuracy of de novo assemblies or scaffolds is quite difficult. One of the key challenges is deciding on the appropriate measure. In this comparison we elect to present several metrics which will likely have different weight depending on the background and intention of the reader.
For the simulated contigs we treat scaffolding as a binary classification problem where methods attempt to predict true adjacencies in the test dataset. The accuracy and sensitivity can be directly measured by computing true positive, and false positive rates. One common summary is MCC, or Mathews Correlation coefficient. This measure assess sensitivity and specificity simultaneously. In the context of scaffolding, this measure illustrates how many correctly ordered and oriented scaffolds were created.
An alternative measure, commonly utilized in genome assembly comparison publications [14, 38] is the notion of corrected N50. Where N50 is the weighted mean scaffold size, the corrected N50 is the same statistic after errors are removed. This can be computed exactly on simulated data, however an alignment based approximation must be used on real test cases.
Finally the usefulness of a genome can also be measured by the number of identifiable biological features captured. Here we capture this measure by recording the percentage of known genes that are found contiguous in the scaffolds.
MCC
While MCC is natural to a computer scientist its useful to a biologist is lacking because the content of the contigs is ignored. A biologist typically asses a scaffold by the N50, Unfortunately this measure does not reflect the accuracy of the scaffolds and rewards aggressive merging. Using MCC or its constituent components as metrics gives greater clarity to the researcher comparing different tools.
N50
The most common metric found in genome assembly and scaffolding is N50. The most recent iteration of benchmark projects have transformed this descriptive number into an accuracy measure by introducing alignment based corrections. Here the scaffolds are aligned against a reference and missalignments are interpreted as orientation, or placement errors. We have developed a more efficient implementation of the correcting method developed by [38]. This enables the tool to be utilized on the NA12878 test case at the cost of accuracy.
The true N50 value can be determined when using simulated contigs by breaking incorrect scaffolds, this measure is denoted as TPN50. An analog to the TPN50 measure can be obtained by aligning the scaffolds against the known reference. Scaffolds (and contigs) are broken at misassembled or misscaffolded regions. This postalignment metrics can be obtained from the assembly evaluation tool called QUAST [38] and it is denoted as NA50.
First the highest ALN50 is always found at bundle size 3 or 5. If the intent of the assembly is to maximize N50 then clearly no algorithm should be run with bundle size less than 3. However, as it was pointed out in both GAGE and QUAST [14, 38], N50 is a misleading metric and alternative measures may be a better judge.
Additionally it can be seen that both OPERA and SILP2 have approximately the same TPN50 in the staph and chr14 test cases, however in rhodo, SILP2 clearly outperforms OPERA and MIP at all bundle sizes. It is not clear why SILP2 performs much better on rhodo, and approximately equivalent on the others. For the complete genome SILP2, OPERA and MIP reported an N50 of 26,235, 39,366, 26,235 respectively. This is consistent with the observations from the synthetic data sets.
Gene reconstruction
An alternative measure of the completeness of a scaffold is the number of genes aligned against the scaffold. For a given percentage of completeness the number of genes found in the corrected scaffold is an indicator of the usefulness of the genome.
The number of reconstructed genes found in the corrected scaffolds for singlegenome datasets.
genome  bundle  SILP2  OPERA  MIP  total 

staph  1  1,727.70  1,168.50  1,545.00  2692 
2  1,727.70  1,168.50  1,559.50  
3  1,727.70  1,210.60  1,575.30  
5  1,727.70  1,262.70  1,584.60  
7  1,727.40  1,280.40  1,588.50  
rhodo  1  2022.7  1618.6  1897.3  3067 
2  2022.7  1618.6  1907  
3  2022.6  1751.1  1894  
5  2022.6  1834.2  1921.3  
7  2022.6  1853.3  1933.3  
chr14  1  350.9    349.6  529 
2  352.00  330.10  350.40  
3  352.40  336.90  350.40  
5  352.40  337.50  351.70  
7  352.40  337.60  3.00  
NA12878 2x  1  30817    30817  34039 
2  30850  30809  30849 
Runtime
Runtime (in seconds) for scaffolding singlegenome datasets.
genome  bundle  SILP1  SILP2  OPERA  MIP 

staph  1  1237  6.4  2538.1  35.8 
2  738  4.5  1456.5  17  
3  305  4  878.5  12.834  
5  142  3.9  386.9  10.54  
7  51  4.3  241  10.115  
rhodo  1  1134  10  2297  118.953 
2  632  4.1  455.2  25.3  
3  486  3.6  5.7  10.995  
5  86  3.4  2  8.778  
7  75  3  1.6  8.217  
chr14  1    64.7    706.3 
2    27.6  99.25  189.685  
3  629  25.5  11  137.67  
5  370  21.5  12  107.85  
7  400  19.25  10.75  94.9875  
NA12878 2x  1    55.2    89.3 
2    1670  76.49  53.28  
3  37751  3878  7875  121.61  
5  27341  3183  4270  134.6  
7  27470  3626  2180  125.66 
On the staph, rhodo and chr14 datasets, it was observed that SILP2 was quicker at higher bundle sizes and no worse than OPERA or MIP at lower bundle sizes. The NA12878 testcase was extremely challenging for all methods and demonstrated the effect of heuristics on large test cases. It is clear from the reduced runtimes that all 3 methods activate some sort of heuristic at lower bundle sizes. The difference between SILP1 and SILP2 is evident at all bundle sizes.
The NA12878 genome was also scaffolded by SILP2 using 20x coverage reads, with a runtime of 18,205 seconds at bundle size 1. Negligible improvement in accuracy over the 2x dataset was observed. From Table 2 it is clear that runtime increases with the complexity of the genome more so than the number of read pairs.
Metagenomics
Metagenomics is the study of genetic material recovered from heterogeneous mixtures often found in nature. Just like in the de novo assembly of a single genome, the accuracy and size of the scaffolds is critical to subsequent analysis steps. Our ILP based solution is flexible enough to include new constraints and objectives to better serve this challenging scenario.
In order to test this hypothesis a simulated metagenomic dataset was created utilizing the staph and rhodo genomes from the GAGE dataset. The simulated contigs used previously were mixed, and both sets of reads were aligned with varying fractions (1.0, 9.5 0.25, 0.0) of staph reads present. Again all three of the major scaffolding tools were tested, however additional weighting scenarios were implemented in SILP2.
Combined runtime and accuracy results for the simulated lowcomplexity metagenome datasets.
METHOD  FRAC STAPH  RUNTIME  SCFN50  ALN50  TPN50  MCC 

SILP2 0  1.00  14.3  51,775.0  20,647  34,495  67.6 
SILP2 0  0.50  13.3  50,450.0  20,103  36,356  69.1 
SILP2 0  0.25  12.7  47,731.0  20,761  35,323  69.3 
SILP2 0  0.00  11.5  21,753.0  13,948  15,649  39.9 
SILP2 1  1.00  14.3  52,557.0  20,655  33,683  67.5 
SILP2 1  0.50  13.8  48,701.0  20,337  35,750  69.0 
SILP2 1  0.25  13.4  49,766.0  20,752  35,146  69.0 
SILP2 1  0.00  11.0  21,925.0  13,847  15,511  39.3 
SILP2 2  1.00  14.3  43,144.0  20,631  31,160  66.3 
SILP2 2  0.50  13.5  42,244.0  20,198  32,477  67.5 
SILP2 2  0.25  13.2  45,137.0  21,161  31,562  67.6 
SILP2 2  0.00  10.8  22,190.0  13,813  16,205  41.6 
SILP2 3  1.00  14.1  43,646.0  19,998  28,856  65.3 
SILP2 3  0.50  13.2  41,893.0  19,790  30,504  66.6 
SILP2 3  0.25  13.0  42,188.0  19,945  30,449  66.4 
SILP2 3  0.00  11.5  21,820.0  13,781  15,635  40.0 
OPERA  1.00  2247.2  15,573.0  13,082  10,386  10.1 
OPERA  0.50  1567.6  13,928.0  12,006  10,440  10.7 
OPERA  0.25  884.0  14,786.0  12,617  10,507  10.5 
OPERA  0.00  544.3  11,121.0  10,720  10,273  4.9 
MIP  1.00  129.9  20,104.0  12,861  18,672  18.4 
MIP  0.50  121.3  19,807.0  12,488  17,613  17.4 
MIP  0.25  114.0  18,520.0  12,269  16,680  17.2 
MIP  0.00  114.1  12,690.0  10,894  12,434  8.7 
BAMBUS2  1.00  1025.89  11,251.0  11,238     
BAMBUS2  0.50  1452.75  10,781.0  10,822     
BAMBUS2  0.25  1676.75  10,806.0  10,834     
BAMBUS2  0.00  2272  11,526.0  11,698     
Interestingly all SILP2 variants fare much better than both OPERA, MIP and BAMBUS2 even with no staph reads present (this differs from results in Figure 5 because the rhodo reads were aligned to both staph and rhodo contigs). It is unclear is the different methodology used in SILP2 sets it apart, or if an implementation quirk throws off the other scaffolders. However across all metrics SILP2 variants perform the best.
In both SILP2 variants and MIP it is observed that the TPN50 decreases as fewer staph reads are utilized. This is expected since there are fewer opportunities to connect staph contigs and both staph and rhodo contigs are used in the calculation of N50. There is no major differences between the variants of SILP2. The coverage based weight seems to improve MCC at the cost of a slightly lowered TPN50 when compared to no weights.
This highly simplified test scenario is not designed to fully explore metagenomic scaffolding, rather to point out an opportunity to further external genome scaffolding algorithms.
Conclusions
Scaffolding in an important step in the de novo assembly pipeline. Biologists rely on an accurate scaffold to perform many types of analysis. The larger the scaffold the more useful it will be to them. Recent advances in de novo assemblers has made it feasible to create draft assemblies for large mammalian genomes. We believe the SILP2, coupled with the most recent scalable assemblers will produce the largest and most complete assemblies. This is made possible utilizing nonserial dynamic programming approach to solve our robust ILP. The ILP formulation for the maximum likelihood model is shown to be flexible enough to handle metagenomic samples.
The future work includes more thorough experimental validation of SILP2 and comparison BAMBUS2 [39] on metagenomic samples. Also we are going to validate SILP2 using methodology and benchmarks from recent paper [40].
Software and data availability
A reference implementation of SILP2 is provided at https://github.com/jimbo/silp2. This implementation relies on the CPLEX optimization library. The code used for generating most of the reported results, including the implementation of the ALN50 metric, is available at https://github.com/jimbo/scafathon. The SILP2 source code along with a small test dataset can also be downloaded from http://dna.engr.uconn.edu/software/SILP2.
Notes
Acknowledgements
This work has been partially supported by Agriculture and Food Research Initiative Competitive Grant no. 20116701630331 from the USDA National Institute of Food and Agriculture and NSF awards IIS0916401 and IIS0916948.
Declarations
The authors declare that funding for publication of the article was by the research institute of the corresponding author.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 9, 2014: Proceedings of the Fourth Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMBSeq 2014). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S9.
Supplementary material
References
 1.Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJ, Birol I: ABySS: a parallel assembler for short read sequence data. Genome research. 2009, 19 (6): 11171123.PubMedCentralCrossRefPubMedGoogle Scholar
 2.Butler J, MacCallum I, Kleber M, Shlyakhter IA, Belmonte MK, Lander ES, Nusbaum C, Jaffe DB: ALLPATHS: De novo assembly of wholegenome shotgun microreads. Genome Research. 2008, 18: 810820.PubMedCentralCrossRefPubMedGoogle Scholar
 3.Gnerre S, MacCallum I, Przybylski D, Ribeiro FJ, Burton JN, Walker BJ, Sharpe T, Hall G, Shea TP, Sykes S, Berlin AM, Aird D, Costello M, Daza R, Williams L, Nicol R, Gnirke A, Nusbaum C, Lander ES, Jaffe DB: Highquality draft assemblies of mammalian genomes from massively parallel sequence data. Proceedings of the National Academy of Sciences. 2011, 108 (4): 15131518.CrossRefGoogle Scholar
 4.Chaisson M, Brinza D, Pevzner P: De novo fragment assembly with short matepaired reads: Does the read length matter?. Genome Res. 2008Google Scholar
 5.Li R, Zhu H, Ruan J, Qian W, Fang X, Shi Z, Li Y, Li S, Shan G, Kristiansen K, Li S, Yang H, Wang J, Wang J: De novo assembly of human genomes with massively parallel short read sequencing. Genome Research. 2010, 20 (2): 265272. http://dx.doi.org/10.1101/gr.097261.109PubMedCentralCrossRefPubMedGoogle Scholar
 6.Zerbino DR, Birney E: Algorithms for de novo short read assembly using de Bruijn graphs Velvet. Genome Research. 2008, 18: 821829.PubMedCentralCrossRefPubMedGoogle Scholar
 7.Bradnam KR, Fass JN, Alexandrov A, Baranay P, Bechner M, Birol I, Boisvert S, Chapman JA, Chapuis G, Chikhi R, Chitsaz H, Chou WCC, Corbeil J, Del Fabbro C, Docking TR, Durbin R, Earl D, Emrich S, Fedotov P, Fonseca NA, Ganapathy G, Gibbs RA, Gnerre S, Godzaridis E, Goldstein S, Haimel M, Hall G, Haussler D, Hiatt JB, Ho IY, Howard J, Hunt M, Jackman SD, Jaffe DB, Jarvis E, Jiang H, Kazakov S, Kersey PJ, Kitzman JO, Knight JR, Koren S, Lam TWW, Lavenier D, Laviolette F, Li Y, Li Z, Liu B, Liu Y, Luo R, Maccallum I, Macmanes MD, Maillet N, Melnikov S, Naquin D, Ning Z, Otto TD, Paten B, Paulo OS, Phillippy AM, PinaMartins F, Place M, Przybylski D, Qin X, Qu C, Ribeiro FJ, Richards S, Rokhsar DS, Ruby JG, Scalabrin S, Schatz MC, Schwartz DC, Sergushichev A, Sharpe T, Shaw TI, Shendure J, Shi Y, Simpson JT, Song H, Tsarev F, Vezzi F, Vicedomini R, Vieira BM, Wang J, Worley KC, Yin S, Yiu SMM, Yuan J, Zhang G, Zhang H, Zhou S, Korf IF: Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species. GigaScience. 2013, 2: 10+PubMedCentralCrossRefPubMedGoogle Scholar
 8.Flicek P, Birney E: Sense from sequence reads: methods for alignment and assembly. Nature Methods. 2009, 6 (11s): S6S12.CrossRefPubMedGoogle Scholar
 9.Lin Y, Li J, Shen H, Zhang L, Papasian CJ, Deng HW: Comparative Studies of de novo Assembly Tools for Nextgeneration Sequencing Technologies. Bioinformatics. 2011Google Scholar
 10.Miller JR, Koren S, Sutton G: Assembly algorithms for nextgeneration sequencing data. Genomics. 2010, 95 (6): 315327.PubMedCentralCrossRefPubMedGoogle Scholar
 11.Paszkiewicz KH, Studholme DJ: De novo assembly of short sequence reads. Briefings in Bioinformatics. 2010, 11 (5): 457472.CrossRefPubMedGoogle Scholar
 12.Pop M: Genome assembly reborn: recent computational challenges. Briefings in Bioinformatics. 2009, 10 (4): 354366.PubMedCentralCrossRefPubMedGoogle Scholar
 13.Schatz MC, Delcher AL, Salzberg SL: Assembly of large genomes using secondgeneration sequencing. Genome Research. 2010, 20 (9): 11651173.PubMedCentralCrossRefPubMedGoogle Scholar
 14.Salzberg SL, Phillippy AM, Zimin A, Puiu D, Magoc T, Koren S, Treangen TJ, Schatz MC, Delcher AL, Roberts M, Mar¸cais G, Pop M, Yorke JA: GAGE: A critical evaluation of genome assemblies and assembly algorithms. Genome Research. 2012, 22 (3): 557567. [http://genome.cshlp.org/content/22/3/557.abstract]PubMedCentralCrossRefPubMedGoogle Scholar
 15.Bashir A, Klammer AA, Robins WP, Chin CS, Webster D, Paxinos E, Hsu D, Ashby M, Wang S, Peluso P: A hybrid approach for the automated finishing of bacterial genomes. Nature Biotechnology. 2012, 701707. 7Google Scholar
 16.Neely RK, Deen J, Hofkens J: Singlemoleculebased methods for mapping genomes Optical mapping of DNA. Biopolymers. 2011, 95 (5): 298311.CrossRefPubMedGoogle Scholar
 17.Huson DH, Reinert K, Myers EW: The greedy pathmerging algorithm for contig scaffolding. J ACM. 2002, 49 (5): 603615.CrossRefGoogle Scholar
 18.Garey MR, Johnson DS, Stockmeyer L: Some simplified NPcomplete problems. Proceedings of the sixth annual ACM symposium on Theory of computing. 1974, STOC '74, New York, NY, USA: ACM, 4763. http://dx.doi.org/10.1145/800119.803884CrossRefGoogle Scholar
 19.Pop M, Kosack DS, Salzberg SL: Hierarchical scaffolding with Bambus. Genome research. 2004, 14: 149159.PubMedCentralCrossRefPubMedGoogle Scholar
 20.Dayarian A, Michael T, Sengupta A: SOPRA: Scaffolding algorithm for paired reads via statistical optimization. BMC Bioinformatics. 2010, 11: 345PubMedCentralCrossRefPubMedGoogle Scholar
 21.Salmela L, M¨akinen V, V¨alim¨aki N, Ylinen J, Ukkonen E: Fast scaffolding with small independent mixed integer programs. Bioinformatics. 2011, 27 (23): 32593265. http://dx.doi.org/10.1093/bioinformatics/btr562PubMedCentralCrossRefPubMedGoogle Scholar
 22.Roy RS, Chen KC, Segupta AM, Schliep A: SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding. arXiv:1111.1426v2[qbio.GN]. 2011, http://doi.acm.org/10.1145/6462.6502Google Scholar
 23.Gao S, Nagarajan N, Sung WK: Opera: reconstructing optimal genomic scaffolds with highthroughput pairedend sequences. Proc 15th Annual international conference on Research in computational molecular biology. 2011, 437451.CrossRefGoogle Scholar
 24.Howison M, Zapata F, Dunn CW: Toward a statistically explicit understanding of de novo sequence assembly. Bioinformatics. 2013, 29 (23): 29592963.CrossRefPubMedGoogle Scholar
 25.Medvedev P, Brudno M: Maximum Likelihood Genome Assembly. Journal of Computational Biology. 2009, 16 (8): 11011116.PubMedCentralCrossRefPubMedGoogle Scholar
 26.Rahman A, Pachter L: CGAL: computing genome assembly likelihoods. Genome Biology. 2013, 14: R8PubMedCentralCrossRefPubMedGoogle Scholar
 27.Shcherbina O: Nonserial Dynamic Programming and Tree Decomposition in Discrete Optimization. OR. 2006, 155160.Google Scholar
 28.Hopcroft JE, Tarjan RE: Dividing a Graph into Triconnected Components. SIAM Journal on Computing. 1973, 2 (3): 135158. http://link.aip.org/link/?SMJ/2/135/1CrossRefGoogle Scholar
 29.Di Battista G, Tamassia R: Online graph algorithms with SPQRtrees. Automata, Languages and Programming. 1990, Springer, 598611.CrossRefGoogle Scholar
 30.Lindsay J, Salooti H, Zelikovsky A, Măndoiu I: Scalable Genome Scaffolding Using Integer Linear Programming. Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine. 2012, BCB '12, New York, NY, USA: ACM, 377383. http://doi.acm.org/10.1145/2382936.2382984CrossRefGoogle Scholar
 31.Langmead B, Salzberg SL: Fast gappedread alignment with Bowtie 2. Nature Methods. 2012, 357359. 4Google Scholar
 32.Chimani M, Gutwenger C, Jünger M, Klein K, Mutzel P, Schulz M: The open graph drawing framework. 15th International Symposium on Graph Drawing. 2007, 2326.Google Scholar
 33.Salmela L, Mäkinen V, Välimäki N, Ylinen J, Ukkonen E: Fast scaffolding with small independent mixed integer programs. Bioinformatics (Oxford, England). 2011, 27 (23): 32593265. http://dx.doi.org/10.1093/bioinformatics/btr562CrossRefGoogle Scholar
 34.CPLEX II: V12. 1: User's Manual for CPLEX. International Business Machines Corporation. 2009, 46 (53): 157Google Scholar
 35.Simpson JT, Durbin R: Efficient de novo assembly of large genomes using compressed data structures. Genome Research. 2012, 22 (3): 549556. [http://genome.cshlp.org/content/22/3/549.abstract]PubMedCentralCrossRefPubMedGoogle Scholar
 36.Langmead B, Salzberg SL: Fast gappedread alignment with Bowtie 2. Nat Meth. 2012, 9 (4): 357359. http://dx.doi.org/10.1038/nmeth.1923CrossRefGoogle Scholar
 37.Gao S, Nagarajan N, Sung WK: Opera: Reconstructing Optimal Genomic Scaffolds with HighThroughput PairedEnd Sequences Research in Computational Molecular Biology. Springer Berlin / Heidelberg, Volume 6577 of Lecture Notes in Computer Science. Edited by: Bafna V, Sahinalp SC, Berlin. 2011, Heidelberg: Springer Berlin / Heidelberg, 437451. http://dx.doi.org/10.1007/9783642200366\40Google Scholar
 38.Gurevich A, Saveliev V, Vyahhi N, Tesler G: QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013, 29 (8): 10721075. http://dx.doi.org/10.1093/bioinformatics/btt086PubMedCentralCrossRefPubMedGoogle Scholar
 39.Koren S, Treangen TJ, Pop M: Bambus 2: scaffolding metagenomes. Bioinformatics. 2011, 27 (21): 29642971.PubMedCentralCrossRefPubMedGoogle Scholar
 40.Hunt M, Newbold C, Berriman M, Otto TD: A comprehensive evaluation of assembly scaffolding tools. Genome Biology. 2014, 15 (3): R42PubMedCentralCrossRefPubMedGoogle Scholar
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